Correlation of the Concentration of Hate Groups with Health Outcomes

This post is a cross post with other blog on the concentration of hate groups in each state (adjusted for population) and their health outcomes.  Pennsylvania is slightly ahead of the national rate at 3.12 groups per million.

This is a follow up on the last post on the number of hate groups (such as the Ku Klux Klan and the Westboro Baptist Church) in each state that are being watched by the Southern Poverty Law Center.  Some may not agree with the inclusion of African American separatists like the Nation of Islam.  If these groups are excluded from the national total (115 out of 939). Computing the population adjusted rate per million gives a rate of 2.62 groups per million for the US.

The state with the highest previous rate of 23.72 groups per million was the District of Columbia.  One possible criticism is that they have a large African American population and that they are not technically a state.  If the four black separatist groups in DC are excluded from their total of 15, it still has a rate of 17.40 groups per million which is well above the national rate.  I decided to look at which other state level variables are correlated with the rate of hate groups in each state.

I combined this data set with a state level health and income data set and several of them are significantly correlated with the health measures.  The strongest of these effects was the one between infant mortality and hate groups per million accounting for 40.9% of the variability.  In the chart on the left, DC is an outlier on both variables. 

The correlation was rerun with DC excluded.  The relationship was still significant but with 12.3% of the variability accounted.  This indicates that the relationship is weaker with DC excluded but still present.

The relationship between hate groups and state level life expectancy was also significant with 29.4% of the variability accounted in a negative relationship where as the number of hate groups increases, the state’s life expectancy decreases.  Like the previous graph, DC is an outlier on hate groups per million.  When DC is removed from the graph, 30.2% of the variability is accounted for in a relationship that is still negative.  This suggests that  DC has high influence but is not poorly fit to the data.

There was no significant correlation between state level per capita income and the rate of hate groups.  Other health related outcomes were significantly associated.  These individual correlations are not described in detail here for space considerations.

There is a more advanced method that can identify clusters of highly correlated variables.  It is called factor analysis.  There were two factors extracted which account for 68.8 % of the variability.  They are presented in the table below.

Rotated Factor Matrixa
(46% of var explained)
(22% of var explained)
Infant Mortality 2007 Deaths/1000
Life Expectancy
% Low Birthweight Babies
Hate Groups per million
Percent under age 65 in 200% of Poverty
Percent Uninsured in Demographic Group for All Income Levels
Expanding medicaid
Extraction Method: Principal Axis Factoring.
 Rotation Method: Varimax with Kaiser Normalization.a
a. Rotation converged in 3 iterations.

The first factor extracted has the health related variables loading on it and accounts for 46% of the total variance.  Infant mortality, life expectancy, % low birth weight babies, and the rate of hate groups load most strongly on this factor.  Percent within 200% of poverty, income, and % uninsured load most strongly on the second extracted factor (called an income factor) while accounting for 22% of the variability.  

The hate group rate does not load on the income factor but it does on the health suggesting an association with health related outcomes.  One must always be careful about inferring a cause and effect relationship based on correlational data. When DC was removed, the factor analysis did not run.

**Related Posts**


A Wave of Hate Groups in California? No in Washington, DC


How do the States Stack Up on Infant Mortality? (Cross Post with PUSH)

A Statistical Profile of the Uninsured in Washington, DC, New Mexico, and Texas